Сравнение методов

	Системная динамика с байесовским подходом	Байесовское моделирование методом Монте-Карло
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s–2010s	1987–1990s
Автор метода	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	O'Hagan, A. and colleagues
Тип	Simulation with probabilistic parameter learning	Simulation / uncertainty quantification
Основополагающий источник	Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014.	O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992
Другие названия	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation
Связанные	6	4
Сводка	Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories.	Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.